UGA Law School

In partnership with the Office of Institutional Research, the below report gives a current outlook on the state of UGA Law School’s admissions pool. Please do not send without permission.

Median Outlook

As of the run of this report, this table gives an outlook of the current deposits and median by median subgroups. Targets are set at 169+/3.9+

group	Admits	Deposit1	Deposit2	TotalApps
Both	144	0	3	144
GPA Splitter	44	0	30	418
LSAT Splitter	193	0	7	210
Neither	37	0	22	993

All Applicants Credentials

The below scatterplot shows the distribution of all applicants and their incoming credentials. UGA Scholars are excluded from the scatterplot. In the legend you can single click to exclude that status or double click to isolate.

To see the distribution of all applicants you can switch to the Histogram

Scatterplot

Show the code

hline <- function(y = 0, color = "black") {
  list(
    type = "line",
    x0 = 0,
    x1 = 1,
    xref = "paper",
    y0 = y,
    y1 = y,
    line = list(color = color)
  )
}

vline <- function(x = 0, color = "black") {
  list(
    type = "line",
    y0 = 0,
    y1 = 1,
    yref = "paper",
    x0 = x,
    x1 = x,
    line = list(color = color)
  )
}

fig<-filter(current_yr,LSAT >0 & !is.na(LSAT))%>%plot_ly(
  type = 'scatter',
  mode = 'markers',
  x = ~LSAT,
  y = ~GPA,
  color = ~App_Status,
  text = ~Name,
  hovertemplate = paste(
    "<b>%{text}</b><br><br>",
    "%{yaxis.title.text}: %{y:.2f}<br>",
    "%{xaxis.title.text}: %{x:.0f}<br>",
    "<extra></extra>"
  )
)%>% layout(shapes = list(hline(3.89),vline(168.5)))
fig

Histogram

Distribution of LSAT Scores

Distribution of College GPAs

Forecasting Applications

Forecast Residuals

The residual plot below measure the fit over time, you’ll see spikes where the applicants at that time period are more or less than the forecast would have anticipated.

residuals_f

`geom_smooth()` using formula = 'y ~ x'

Forecast by LSAT bin

The below graphs are historical and forecasted amount of applicants for each LSAT group and Residency status.

fore_f

Total Forecast

ggplotly(fore_y)